Impact of China’s slowdown on the Global Economy: Modified GVAR Approach
Bibliographic record
Abstract
Asia-Pacific region is much anxious about China’s slowdown, but the rest of the world has definite reason to worry about the consequences of the slowdown in China. During last few decades China is strongly integrated with Asia and also with the rest of the World. This paper investigates what the impact of China’s slowdown on the global economy is. If any crisis in China, how much does it affect developed and emerging or developing economies? Using modified Global VAR (GVAR) model, this paper focuses on these issues. This study considers more on international linking variables for the period of 2000-2012. Evidence based on GVAR analysis for six developed countries (G6: USA, UK, Germany, Japan, Canada and Australia) and BRICS (G4: Brazil, Russia, India and South Africa) show that the impact of China’s slowdown is more on emerging BRICS nations than that of developed economies. Impact of China’s GDP growth shock on the rest of emerging Asia is more since it has a strong production network in East and South East Asia. So, China’s slowdown certainly affects Asia more than western developed economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".